Zero-touch payroll management system

ABSTRACT

A system and method for zero-touch payroll management that enables an employer to automate payroll recording, approval, and disbursement, while giving employees increased flexibility in their working hours and pay scheduling and enabling processing of payroll based on real-time and predicted business cashflow.

CROSS-REFERENCE TO RELATED APPLICATIONS

Priority is claimed in the application data sheet to the followingpatents or patent applications, the entire written description of eachof which is expressly incorporated herein by reference in its entirety:

-   Ser. No. 17/155,253-   63/005,891-   Ser. No. 17/153,213-   63/005,899

BACKGROUND Field of the Art

The disclosure relates to the field of computer-based optimization, andmore particularly to the field of computerized payroll management forbusiness establishments.

Discussion of the State of the Art

In business operations, employees are typically paid according to setrates of pay and scheduled working hours, and alterations to ordeviations from their scheduled working hours require manual adjustmentof their payroll calculation to avoid over- or under-payment. Employeesalso must be paid on a fixed schedule, creating a situation in which anemployer must dedicated a period of time to managing payroll for eachpay period, time that could otherwise be spent furthering the business.Furthermore, if the employer has insufficient funds to process payrollat the time it is scheduled, an employee may not be paid properly and beforced to wait until cashflow allows the remainder of their pay to beprocessed.

There is currently no system that enables businesses to easily managepayroll while providing increased flexibility for employees andconsistency for employers without the added manual step of accommodatingtheir scheduling within a payroll system.

What is needed is a system and method for zero-touch payroll managementthat enables an employer to automate payroll recording, approval, anddisbursement, while giving employees increased flexibility in theirworking hours and pay scheduling and enabling processing of payrollbased on real-time and predicted business cashflow.

SUMMARY

Accordingly, the inventor has conceived, and reduced to practice, asystem and method for zero-touch payroll management. The system is acloud-based network containing a predictive cashflow management engine,payment engine, predictive inventory management engine, inventoryoptimization engine, predictive staffing management engine, staffoptimization engine, mobile and compute devices for restaurants, staffand vendors, gateways for vendors and staff to interface with financialinstitutions and other 3rd party businesses, enterprise database tostore and retrieve including financial data, staffing data, andinventory data. Taken together or in part, said system optimizesbusiness operations by predicting and optimizing in real-time keyoperational decisions around financial, staffing, and inventorymanagement based upon a multitude of variables associated with thebusiness enterprise. The system may be accessed through web browsers orpurpose-built computer and mobile phone applications.

According to a first preferred embodiment, a system for zero-touchpayroll management is disclosed, comprising: a payment engine comprisinga first plurality of programming instructions stored in the memory andoperating on the processor, wherein the first plurality of programminginstructions, when operating on the processor, causes the computersystem to: receive a precise work time based on clock-in and clock-outtimes of a staff member; retrieve a staff profile for the staff memberfrom a database, wherein the staff profile comprises a rate of pay, apayroll deduction, a tax rate, and a payment profile; update the paymentprofile by querying an employee device, the payment profile comprising aset of dates for a preferred split payment and payment method set by theemployee device; calculate a net pay for the staff member, the net paybeing based on the information in the staff profile; if the net pay isgreater than a balance in a payment account, retrieve and process amicro-loan offer, wherein the micro-loan offer provides at least theamount difference between the balance and the net pay; and disburse apayroll deposit, wherein the preferred split payment amount istransferred from the payment account to the payment method.

According to a second preferred embodiment, a method for zero-touchpayroll management is disclosed, comprising the steps of: receiving at apayment engine, a precise work time based on clock-in and clock-outtimes of a staff member; retrieving a staff profile for the staff memberfrom a database, wherein the staff profile comprises a rate of pay, apayroll deduction, a tax rate, and a payment profile; updating thepayment profile by querying an employee device, the payment profilecomprising a set of dates for a preferred split payment and paymentmethod set by the employee device; calculating a net pay for the staffmember, the net pay being based on the information in the staff profile;if the net pay is greater than a balance in a payment account, retrieveand process a micro-loan offer, wherein the micro-loan offer provides atleast the amount difference between the balance and the net pay; anddisbursing a payroll deposit, wherein the preferred split payment amountis transferred from the payment account to the payment method.

According to various aspects of the invention, wherein the paymentmethod is a payment card; wherein the payment engine processes atransaction made using the payment card; wherein the clock-in or clockout time is received from a biometric scanning device; and wherein thesystem and method further comprise retrieving a loan risk profile forthe staff member; issuing a micro-loan based on the loan risk profile;and updating the staff profile based on the issued micro-loan, whereinthe updated staff profile comprises at least a payroll deduction basedon the micro-loan issued.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawings illustrate several aspects and, together withthe description, serve to explain the principles of the inventionaccording to the aspects. It will be appreciated by one skilled in theart that the particular arrangements illustrated in the drawings aremerely exemplary, and are not to be considered as limiting of the scopeof the invention or the claims herein in any way.

FIG. 1 is a block diagram illustrating an exemplary system architecturefor a real-time finance, inventory and staffing management system.

FIG. 2 is a block diagram illustrating an exemplary architecture for anaspect of a real-time financial system.

FIG. 3 is a block diagram illustrating an exemplary architecture for anaspect of a real-time inventory management system.

FIG. 4 is a block diagram illustrating an exemplary architecture for anaspect of a real-time staffing management system.

FIG. 5 is a flow diagram showing the steps of an exemplary method forreal-time predictive cash flow for a restaurant business from initialreceipt of income and expense data through analysis of income andexpense data.

FIG. 6 is a flow diagram showing the steps of an exemplary method forreal-time predictive cash flow for a restaurant business fromcalculation of real-time cash flow through notification of staffingchanges to restaurant staff.

FIG. 7 is a flow diagram showing the steps of an exemplary method forreal-time payment engine for a restaurant business from initial receiptof payment data through sending payments to vendors and restaurantstaff.

FIG. 8 is a flow diagram showing the steps of an exemplary method forpredictive inventory management for a restaurant business from initialreceipt of supplier data through sending shopping lists to vendors fororder fulfillment.

FIG. 9 is a flow diagram showing the steps of an exemplary method forreal-time inventory optimization for a restaurant business from initialreceipt of supplier data through sending shopping lists to vendors fororder fulfillment.

FIG. 10 is a flow diagram showing the steps of an exemplary method forpredictive staffing management for a restaurant business from initialreceipt of supplier data through sending shopping lists to vendors fororder fulfillment.

FIG. 11 is a flow diagram showing the steps of an exemplary method forreal-time staffing optimization for a restaurant business from initialreceipt of supplier data through sending shopping lists to vendors fororder fulfillment.

FIG. 12 is a flow diagram illustrating the steps of an exemplary methodfor zero-touch payroll management, detailing a process for trackingemployee work hours and performing real-time scheduling adjustments.

FIG. 13 is a flow diagram illustrating the steps of an exemplary methodfor zero-touch payroll management, detailing a process for calculatingand disbursing pay to employees.

FIG. 14 is a flow diagram illustrating the steps of an exemplary methodfor zero-touch payroll management, detailing a process for providing amicro-loan to an employee.

FIG. 15 is a block diagram illustrating an exemplary architecture for apayment engine with an automated split payment aspect.

FIG. 16 is a flow diagram illustrating the steps of an exemplary methodfor zero-touch payroll management, detailing a process for providingautomated split payments to an employee.

FIG. 17 is a block diagram illustrating an exemplary hardwarearchitecture of a computing device.

FIG. 18 is a block diagram illustrating an exemplary logicalarchitecture for a client device.

FIG. 19 is a block diagram showing an exemplary architecturalarrangement of clients, servers, and external services.

FIG. 20 is block diagram illustrating another aspect of an exemplaryhardware architecture of a computing device.

DETAILED DESCRIPTION

The inventor has conceived, and reduced to practice, a system and methodfor zero-touch payroll management that enables an employer to automatepayroll recording, approval, and disbursement, while giving employeesincreased flexibility in their working hours and pay scheduling.

One or more different aspects may be described in the presentapplication. Further, for one or more of the aspects described herein,numerous alternative arrangements may be described; it should beappreciated that these are presented for illustrative purposes only andare not limiting of the aspects contained herein or the claims presentedherein in any way. One or more of the arrangements may be widelyapplicable to numerous aspects, as may be readily apparent from thedisclosure. In general, arrangements are described in sufficient detailto enable those skilled in the art to practice one or more of theaspects, and it should be appreciated that other arrangements may beutilized and that structural, logical, software, electrical and otherchanges may be made without departing from the scope of the particularaspects. Particular features of one or more of the aspects describedherein may be described with reference to one or more particular aspectsor figures that form a part of the present disclosure, and in which areshown, by way of illustration, specific arrangements of one or more ofthe aspects. It should be appreciated, however, that such features arenot limited to usage in the one or more particular aspects or figureswith Headings of sections provided in this patent application and thetitle of this patent application are for convenience only, and are notto be taken as limiting the disclosure in any way.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or morecommunication means or intermediaries, logical or physical.

A description of an aspect with several components in communication witheach other does not imply that all such components are required. To thecontrary, a variety of optional components may be described toillustrate a wide variety of possible aspects and in order to more fullyillustrate one or more aspects. Similarly, although process steps,method steps, algorithms or the like may be described in a sequentialorder, such processes, methods and algorithms may generally beconfigured to work in alternate orders, unless specifically stated tothe contrary. In other words, any sequence or order of steps that may bedescribed in this patent application does not, in and of itself,indicate a requirement that the steps be performed in that order. Thesteps of described processes may be performed in any order practical.Further, some steps may be performed simultaneously despite beingdescribed or implied as occurring non-simultaneously (e.g., because onestep is described after the other step). Moreover, the illustration of aprocess by its depiction in a drawing does not imply that theillustrated process is exclusive of other variations and modificationsthereto, does not imply that the illustrated process or any of its stepsare necessary to one or more of the aspects, and does not imply that theillustrated process is preferred. Also, steps are generally describedonce per aspect, but this does not mean they must occur once, or thatthey may only occur once each time a process, method, or algorithm iscarried out or executed. Some steps may be omitted in some aspects orsome occurrences, or some steps may be executed more than once in agiven aspect or occurrence.

When a single device or article is described herein, it will be readilyapparent that more than one device or article may be used in place of asingle device or article. Similarly, where more than one device orarticle is described herein, it will be readily apparent that a singledevice or article may be used in place of the more than one device orarticle.

The functionality or the features of a device may be alternativelyembodied by one or more other devices that are not explicitly describedas having such functionality or features. Thus, other aspects need notinclude the device itself.

Techniques and mechanisms described or referenced herein will sometimesbe described in singular form for clarity. However, it should beappreciated that particular aspects may include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. Process descriptions or blocks in figures should beunderstood as representing modules, segments, or portions of code whichinclude one or more executable instructions for implementing specificlogical functions or steps in the process. Alternate implementations areincluded within the scope of various aspects in which, for example,functions may be executed out of order from that shown or discussed,including substantially concurrently or in reverse order, depending onthe functionality involved, as would be understood by those havingordinary skill in the art.

While the use case of a restaurant business owner optimizing theirbusiness operations is a primary example used herein, it is important tonote that the invention is not so limited, and may be used by anybusiness (i.e., the invention is not limited to restaurants, and can beapplied to any retail goods, such as grocery stores, on-line and/orbrick and mortar; service business, such as home cleaning, lawn care,financial services) seeking to optimize their staffing and payroll in azero-touch fashion.

Definitions

“Business establishment” or “place of business” as used herein mean thelocation of any business entity with which customers may transactbusiness. Typically, this will be a physical location where customersmay enter the location and transact business directly with employees ofthe business, however, it may also be a delivery-based business. Manyexamples herein use a restaurant as the business establishment, but theinvention is not limited to use in restaurants, and is applicable to anybusiness establishment. “Patron” is used to reference the customer orprospective customer of the business establishment. “Staff” is used toreference the employee or contractor of the business establishment.

Conceptual Architecture

FIG. 1 is a block diagram illustrating an exemplary system architecture110 for a real-time finance, inventory and staffing management system,according to a preferred aspect. According to an aspect, and using arestaurant as an exemplary business establishment, system 110 comprisesa real-time financial system 200, a real-time inventory managementsystem 300, a real-time staffing management system 400, and databases150. Staff mobile devices 120 and restaurant mobile devices 130, mayconnect to real-time finance, inventory and staffing management system110, typically via a cellular phone network 160, although connectionsmay be made through other means, as well, such as through Internet 170(e.g., through a Wi-Fi router). Restaurant computers 140 and/or vendorcomputers 160 may connect to real-time finance, inventory, and staffingmanagement system 110, typically through an Internet 170 connection,although other network connections may be used.

According to an aspect, a restaurant may access restaurant computer 140to enter or update a variety of financial information that may includelease hold costs, loaded labor rates, cost of goods sold menu items,recipe information, inventory on-hand, staff, staffing needs, culinaryskill requirements along with other information that may be stored in adatabase 150, and used by real-time financial system 200 that may offeror execute financial transactions to optimize business operations aroundone or more business metrics.

Similarly, according to an aspect, a restaurant may access restaurantcomputer 140 to enter or update a variety of inventory information thatmay include current inventory on hand, re-order levels, expected usageand so on along with other information that may be stored in a database150, and used by real-time inventory management system 300 that mayoffer or execute inventory related actions to optimize inventoryoperations around one or more business metrics.

Similarly, according to another aspect, a restaurant manager may accessrestaurant computer 140 to enter or update a variety of operationalinformation that may include staffing needs, training needs, hours ofoperations, upcoming market events and so on along with otherinformation that may be stored in a database 150, and used by real-timestaffing management system 400 that may offer or execute staffingactions to optimize business operations around one or more staffingmetrics. Other such real-time information and factors may also bedetermined by system through access to one or more external resources180 such as a utility provider that may include current usage, currentrates, balance due and so forth. Other exemplary external resources maycomprise public data services or sources such as weather service, socialmedia platforms, news outlets and rating services.

Likewise, vendors may access vendor computer 160 to enter informationabout service or product provided, invoice information, credit terms andany current or upcoming promotions along with other information.Examples of the types of information that a vendor may enter include,but are not limited to: restaurant name, location, types of food itemprovided (e.g. rice, beans, wagyu beef, scallions, grass fed youngchickens, chicken liver pate, cod liver oil), type of non-food itemprovided (e.g. 12 oz paper cups, 9 inch paper dinner plate, bleach,lemon wax, rubbing alcohol) service offered (e.g. uniform service,landscaping maintenance), item pricing, credit terms, special pricingoptions like volume discounting daily specials or seasonal offerings. Insome aspects, the system may be able to determine certain information byaccessing external resources 180 such as mapping websites andapplications. For example, system may access a publicly-availablemapping website such as Google maps, which may contain information aboutthe restaurant's name, location, types of food offered, hours ofoperation, phone number, etc. Thus, in some aspects, it is not necessaryfor the restaurant to enter certain information through portal, as theinformation may be automatically obtained from external resources 180.

In an aspect, when a staff mobile device 120 connects to real-timefinance, inventory and staffing management system 110 and requests amicroloan, real-time financial system 200 queries database 150 for therisk profile of staff, current cash profile of the restaurant andcompares with loan request and offers a loan and associated terms. Thestaff may decide to accept or decline loan term offers through the staffmobile device 120. Similarly, staff may access staff mobile device 120and request scheduling information or updates from the real-timestaffing management system 400.

In some aspects, real-time staffing management system 400, through staffmobile device 120, may also provide information to the staff of schedulemodifications or upcoming staffing needs which the staff may accept ordecline. If the restaurant has entered information such as incentivepay, real-time staffing management system, 400 may use that informationto offer the restaurant staff additional monetary or other incentives(e.g. future vacation day with pay) to accept a shift schedule that issorely needed to be filled. Such incentives may be adjusted for busyperiods at the restaurant (typically around lunch and dinner) eitherautomatically based on the restaurant's history as stored in a database150, or by retrieving information stored in a database 150 that has beenmanually entered by the restaurant through restaurant computer 140 orrestaurant mobile device 130.

FIG. 2 is a block diagram illustrating an exemplary architecture for anaspect of a real-time financial system 200. According to an aspect, areal-time financial system 200 comprises several subsystems including apredictive cash flow management engine 210 and a payment engine 220. Apredictive cashflow management engine 210 may connect, for a bi-lateraldata exchange, between POS data gateway 201, accounts receivable gateway202, account payable gateway 203, financial services gateway 204; mayreceive operational cost profile 205 and staffing profile 206; and mayprovide as outputs loan risk profile 207, menu adjustment 208, andstaffing adjustment 209. A payment engine 220 may receive payee profile212, staff profile 213; establish a bi-lateral data exchange withrestaurant gateway 214, microloan gateway 215, and payment gateway 216.

According to an aspect, when a restaurant desires to optimize theiroperations around a given parameter, for example to maximizeprofitability, predictive cashflow management engine 210 receivescurrent menu offering from POS data gateway 201, current outstandinginvoices from account receivable gateway 202, current outstandingaccounts payable from accounts payable gateway 203, current bankingbalances from financial services gateway 204, operational cost profile205, staffing profile 206. Input data set is cleansed and formattedusing machine learning or other techniques to those skilled in the art.A predictive cashflow engine then uses dimension gradient optimizationtechnique to maximize a profitability function. The predictive cashflowmanagement engine will provide a corresponding set of instructions formenu adjustments 208, staffing adjustments 209, and makes updatedproposals to suppliers through accounts payable gateway 203 to maximizeprofitability around a given set of parameters.

In some aspects, real-time financial system 200 engine may have apayment subsystem in which a payment engine 220 may process payroll and(if needed) make a loan to a restaurant staff or other restaurantpartner through a microloan gateway 215 based upon information receivedfrom the loan risk profile 207 via the predictive cashflow managementengine 210. Loan risk profile 207 may be determined by deep learningalgorithms such as elastic net, random forest, or gradient boostingmodels or other approaches known to those skilled in the art. Paymentsmay also be automatically made to a supplier or other business contactbased on information in their respective payee profile 212, for exampleto automatically reorder products based on real-time inventoryinformation as described below, with reference to FIG. 8.

Note that this example is simplified for clarity, and that real-timefinancial system 200 will address a much broader set of factors andvariables, as described elsewhere herein. The predictive cashflowmanagement engine 210 may use any number of optimization algorithms,including machine learning algorithms or others known in the art, tofind optimal solutions to the large number of variables presented.

FIG. 3 is a block diagram illustrating an exemplary architecture for anaspect of a real-time inventory management system 300. According to anaspect, real-time inventory management system 300 comprises, apredictive inventory management engine 310, inventory optimizationengine 320 that may connect to POS data gateway 301, supplier gateway302, inventory data 303, recipe data 304, patron data 305, 3′d partygateway 306 and restaurant portal 312; may provide as outputs shoppinglist 311, menu adjustment 321. Further information regarding thepredictive inventory management engine 310 and inventory optimizationengine 320 are found in FIG. 8 and FIG. 9, respectively.

FIG. 4 is a block diagram illustrating an exemplary architecture for anaspect of a real-time staffing management system 400. According to anaspect, real-time staffing management system 400 comprises, a predictivestaffing management engine 410 and staffing optimization engine 420. Apredictive staffing management engine 410 may connect to staffingresource data 401, patron data 402, and 3^(rd) party gateway 403,provide as output staffing requirements 411 and staff updatenotification 412; a staff optimization engine 420 may connect to POSdata gateway 404 and current staffing data 405, may provide as outputstaff adjustments 421. Staffing optimization engine 420 may handle anumber of staffing tasks including, but not limited to, adjustingstaffing assignments to optimally meet the current needs of a business,tracking actual staff working hours, and providing staff working hourinformation to payment 220 for use in processing payroll, taxes, orother related tasks. Further information regarding the predictivestaffing management engine 410 and staffing optimization engine 420 arefound in FIG. 10 and FIG. 11, respectively.

Detailed Description of Exemplary Aspects

FIG. 5 is a flow diagram showing the steps of an exemplary method forpredictive cashflow. In a first step, 501 receive point-of-sales (“POS”)data from a plurality of business computing devices for one or moresales transactions, POS data for each sales transaction comprising fooditem, food amount, food cost, time and location descriptors; In a nextstep, 502 receive accounts receivable data from a plurality of businesscomputing devices for one or more business vendors, accounts receivabledata comprising account receivable entity name, service rendered,product delivered, amount paid, balance due and credit terms. In a nextstep, 503 receive accounts payable data from a plurality of businesscomputing devices for one or more business vendors, accounts payabledata comprising account payable entity name, service rendered, productdelivered, amount paid, balance due and credit terms. In a next step,504 receive bank and credit card account data from a plurality ofbusiness computing devices for one or more banking entities, bank andcredit card account data comprising account balances, payments receivedand balance. In a next step, 505 retrieve operational cost profile froman operational database 150, operational cost profile comprising hoursof operation, leasehold costs, tax and insurance costs, cost of goodssold, profit margins, salary costs, hourly wage costs, professionalservices costs. In a next step, 506 retrieve staffing profile from astaffing management database 150, staffing profile comprising number ofhourly staff previously scheduled, hourly staff previously worked, staffskill-level, planned hourly staff schedule. In a next step, 507 analyzeincome and expense data that may include deep learning techniques suchas recurring neural networks or other techniques familiar to thoseskilled in the art.

FIG. 6 is a flow diagram showing the steps of an exemplary method forpredictive cashflow continuation of FIG. 5. In a first step, 601calculate real-time profit and loss, cash flow data. In a next step, 602generate loan risk profile using deep learning algorithms such aselastic net, random forest, or gradient boosting models or otherapproaches known to those skilled in the art, and provide recommendationfor micro-loans based on cash flow to optimize business operations. In anext step, 603 generate menu adjustment to optimize business operations.In a next step, 604 generate staffing adjustment to optimize businessoperations. In a next step, 605 send microloan details to paymentgateway. In a next step, 606 send menu adjustments to restaurant displayboards and back office food preparation display devices. In a next step,607 send staffing adjustments to business compute device, recommendationcomprising staffing skill level, schedules, cost impact. In a next step,608 notify staff of schedule changes via their mobile device.

FIG. 7 is a flow diagram showing the steps of an exemplary method forreal-time payment processing. In a first step, 701 receive payment datafrom a predictive cash flow management engine, payment data comprisingpayor name and address, payee identifier, invoice identifier, andpayment amount. In a next step, 702 receive micro-loan data from apredictive cash flow management engine, micro-loan data comprisingemployees name and address, employee identifier, loan identifier, andloan amount. In a next step, 703 retrieve payee profile data from payeeprofile database 150, payee profile data comprising payees name, payeeaddress, open invoices, balances due, payment preference details. In anext step, 704 retrieve staff profile data from staff profile database,staff profile data comprising staff name, staff address, open loanbalances, repayment terms, payment due date, bank account details. In anext step, 705 compute payment details and distribution amounts. In anext step, 706 send electronic payment information to business computedevice, electronic payment comprising bank routing number, bank accountnumber, dollar amount, reference memo. In a next step, 707 sendelectronic payment to business check printing, electronic paymentcomprising payee name, payee address, invoice number, payment amount.

FIG. 8 is a flow diagram showing the steps of an exemplary method forpredictive inventory management process. In a first step, 801 receivepoint-of-sales (“POS”) data from a plurality of business computingdevices for one or more sales transactions, POS data for each salestransaction comprising food item, food amount, food cost, time andlocation descriptors. In a next step, 802 receive supplier data from aplurality of business computing devices for one or more supplierbusiness entities, supplier data for each supplier business entitycomprising vendor name, vendor location, food item, available quantity,list price, volume discounts. In a next step, 803 receive real-time3^(rd) party data from a plurality of business computing devices for oneor more data sources, the 3^(rd) party data comprising local news andevents, current and forecasted weather, social media feeds, rating andreview sites. In a next step, 804 retrieve inventory data from inventorydatabase, inventory data comprising items on-hand, par level, lastre-order date, expiration date, forecasted re-order date. In a nextstep, 805 retrieve patron data from patron profile database, patron datacomprising food items previously purchased, day and time data, weatherconditions, local news and events. In a next step, 806 retrieve recipedata from recipe database, recipe data comprising food type, foodamount. In a next step, 807 analyze inventory metrics, patron historicalfood purchase history. In a next step, 808 predict future inventoryrequirements. In a next step, 809 generate recommended shopping list tooptimize business operations. In a next step, 810 send recommendedshopping list to business compute device, recommended shopping listcomprising vendor name, vendor address, order item, order quantity,payment terms delivery options available. In a next step, 811 sendrecommended shopping list to supplier business compute device for orderfulfillment, recommended shopping list comprising vendor name, vendoraddress, order item, order quantity, payment terms delivery optionsavailable.

FIG. 9 is a flow diagram showing the steps of an exemplary method forinventory optimization process. In a first step, 901 receivepoint-of-sales (“POS”) data from a plurality of business computingdevices for one or more sales transactions, POS data for each salestransaction comprising food item, food amount, food cost, time andlocation descriptors. In a next step, 902 retrieve inventory data frominventory database, inventory data comprising items on-hand, par level,last re-order date, expiration date, forecasted re-order date. In a nextstep, 903 retrieve recipe data from recipe database, recipe datacomprising food type, food amount. In a next step, 904 retrieve patrondata from patron profile database, the patron data comprising food itemspreviously purchased, day and time data, weather conditions, surroundingcircumstances including local news and events. In a next step, 905receive real-time 3^(rd) party data from a plurality of businesscomputing devices for one or more data sources, the 3^(rd) party datacomprising local news and events, current and forecasted weather, socialmedia feeds, rating and review sites. In a next step, 906 analyzeinventory metrics, patron food purchase history and predict inventoryrequirements using Deep Learning algorithms such as elastic net, randomforest, or gradient boosting models or other Artificial Intelligenttechniques known to those skilled in the art. In a next step, 907generate recommended menu adjustments to optimize business operations.In a next step, 908 send recommended menu adjustments to businesscompute device, the recommended menu adjustments comprising food itemname, price, promotion information.

FIG. 10 is a flow diagram showing the steps of an exemplary method forpredictive staff management process. In a first step, 1001 receivestaffing data from a plurality of business computing devices for one ormore staffing business entities, staffing data for each staffingbusiness entity comprising staff name, skillset, availability,compensation details. In a next step, 1002 retrieve aggregate patrontransaction data from patron transaction database, patron transactiondata comprising patron dining dates and times, menu items purchased,amount paid, number in party, dining experience rating, weatherconditions, local news and events. In a next step, 1003 receivereal-time 3^(rd) party data from a plurality of business computingdevices for one or more data sources, 3^(rd) party data comprising localnews and events, current and forecasted weather, social media feeds,rating and review sites. In a next step, 1004 analyze staffing metrics,patron dining data and create predicted data set using Deep Learningalgorithms such as elastic net, random forest, or gradient boostingmodels or other Artificial Intelligent techniques known to those skilledin the art. In a next step, 1005 generate recommended staffingrequirements to optimize business operations. In a next step, 1006 sendstaffing requirements to business compute device, staffing requirementslist comprising staff name, staff contact info, skillset, availability,and compensation details. In a next step, 1007 send personalizedstaffing info to staff compute device for schedule notification andacceptance to staff member.

FIG. 11 is a flow diagram showing the steps of an exemplary method forstaff optimization process. In a first step, 1101 receive point-of-sales(“POS”) data from a plurality of business computing devices for one ormore sales transactions, POS data for each sales transaction comprisingfood item, food amount, food cost, time and location descriptors. In anext step, 1102 receive patron real-time experience data from aplurality of business computing devices for one or more diningtransactions, patron real-time experience data for each transactioncomprising rating data, food items ordered, food cost, time and locationdescriptors. In a next step, 1103 receive schedule forecast data frompredictive staffing management engine. In a next step, 1104 retrievereal-time staffing data from staffing database, real-time staffing datacomprising staff on-hand, skill level, shift detail, compensationdetails. In a next step, 1105 analyze current staffing metrics, currentpatron dining experience and compare with optimized business operationmetrics. In a next step, 1106 generate recommended staffing adjustmentsto optimize business operations. In a next step, 1107 send staffingadjustments to business compute device, staffing adjustments comprisingstaff name, recommended change, expected outcome.

An exemplary financial forecasting technique may include applying Bayes'Theorem or similar financial forecasting technique familiar to thoseskilled in the art. In making a particular business decision (e.g.extending a microloan to a staff member, paying a vendor bill, making afood item purchase, etc.), one can forecast the resulting financialoutcome (e.g. changes to restaurants' net income stream, balance sheets,bond credit rating, changing value of assets and other related financialdata) with a high probability of occurring.

An exemplary semantic comparison method may include term vector spaceanalysis technique to those familiar in the art. Term vector modeling isan algebraic model for representing text and text documents as vectors.Each term or word in a text document typically corresponds to adimension in that vector. Once a text document is described as a wordvector, comparisons between two vectors may be made using vectorcalculus. One useful technique to determine similarities betweendocuments is by comparing the deviation of angles between each documentvector and the original query vector where the query is represented as avector with same dimension as the vectors that represent the otherdocuments.

An exemplary dimensional reduction technique familiar to those skilledin the art is Principal Component Analysis (“PCA”), which may be used tooptimize the variables prior to vectorization to reduce dimensionalityof resulting vectors prior to feeding into a machine learning algorithm.

An exemplary optimization method may include deep learning techniquesfamiliar to those skilled in the art. One such form of deep learningthat is particularly useful when generating text is Recurrent NeuralNetworks (“RNN”) using long short-term memory (“LSTMs”) units or cells.A single LSTM is comprised of a memory-containing cell, an input gate,an output gate and a forget gate. The input and forget gate determinehow much of incoming values transit to the output gate and theactivation function of the gates is usually a logistic function. Theinitial input data will cause the model to learn the weights ofconnections that influence the activity of these gates which will impactthe resultant output. To generate optimized staffing for a givenrestaurant, historic staffing for a given restaurant, patron loading,patron rating review along with other data are fed into the input gateof the RNN, in turn the RNN will learn how best to staff for givenrestaurant situation and create an optimized staffing output.

FIG. 12 is a flow diagram illustrating the steps of an exemplary methodfor zero-touch payroll management, detailing a process for trackingemployee work hours and performing real-time scheduling adjustments. Ina first step 1201, a staffing optimization engine 420 may record whenemployees log in to an app or web portal to begin their shift, orsimilar forms of “clocking in” at start of shift. This may be used inconjunction with real-time staffing level tracking as described above,with reference to FIG. 4, for example to update staffing information1202 in real-time the moment an employee begins their shift, ensuringthat current staffing information is kept up-to-date in real-time sothat current and scheduled staffing assignments and hours can beadjusted as needed to ensure optimal business operation. When anemployee clocks out 1203, staffing levels may again be updated inreal-time and any necessary adjustments may be applied 1204 such asreassigning other staff members or adjusting staffing schedules (forexample, to accommodate a staffing deficiency if an employee leavesearly, or does not show up at the scheduled beginning of their shift),and the employee's time worked may be calculated and logged 1205. Thistime-worked record may then be provided 1206 to a payment engine 220,which may calculate and disburse the employee's pay based on theiractual time worked, according to a method described below with referenceto FIG. 13.

FIG. 13 is a flow diagram illustrating the steps of an exemplary methodfor zero-touch payroll management, detailing a process for calculatingand disbursing pay to employees. A payment engine 220 may receiveprecisely-tracked employee working hours 1301 from a staffingoptimization engine 420, and may then retrieve 1302 the employee'sstored staff profile 213 to load information such as (for example) theirrate of pay, tax information, and payment methods on file. Paymentengine 220 may then calculate the employee's gross pay for the loggedtime period 1303, taking into account the precise time worked and theirknown rate of pay; for example, this enables precise payment ofemployees that may have hourly or daily rates of pay, by calculating theprecise portion of the pay increment that was actually worked. Anyapplicable taxes or other deductions may then be calculated 1304,producing the employee's net pay for the hours worked. A payment methodmay be selected 1305 from the employee's staff profile, for examplebased on stored preferences such as instructions to attempt to use anemployee's debit card first, and if the transaction fails for any reasonto then use their bank account information. The employee may then bepaid precisely based on actual (rather than scheduled) working timebased on real-time tracking, and pay may be disbursed 1306 via theselected payment method for the employee such as (for example, includingbut not limited to) the employee's bank account, a payment service orapp such as PAYPAL™, VENMO™, or SQUARE™, or via a stored debit, credit,or prepaid card to which funds may be directly deposited. Payment mayalso be disbursed according to stored pay schedule information in theemployee's staff profile 213, for example instructions to disburse payon a biweekly, weekly, or daily basis. The use of precise time tracking(as described above, with reference to FIG. 12) and stored pay andscheduling information enables fine-grained payment based on actualstaff working hours, so that employees are automatically paid accordingto their time worked and an employer need not review payroll to adjustpay manually based on any discrepancies between scheduled and actualworking hours. This provides a zero-touch payroll system in whichneither the employee nor the employer must perform any manualinterventions to process payroll, and pay is precisely calculated anddisbursed automatically according to preconfigured rules and paymentinformation. For an employee this can provide an extra level ofconvenience if they receive pay through a credit, debit, or prepaidpayment card; their pay can be directly deposited to the card when theyclock out from a shift, so they immediately have access to the preciseamount of money they earned based on real-time tracking of their workinghours and automated calculation and disbursal of precise payroll.

In some instances, an employer may lack sufficient funds in theirpayment account to process a payroll transaction 1307. In such a case,payment engine 220 may process a loan offer produced by predictive cashflow management engine 210, ensuring payroll may be processed timelywhile simultaneously ensuring business operations are not impacted byutilizing a loan offer that takes actual business cashflow intoconsideration, as described above with reference to FIG. 6.

FIG. 14 is a flow diagram illustrating the steps of an exemplary methodfor zero-touch payroll management, detailing a process for providing amicro-loan to an employee. In some instances, an employee may utilize adebit, credit, or prepaid payment card to receive their pay. This cardmay also be used to process transactions normally, and when doing so atransaction fee may be received by the issuing employer (in this mannerit can be appreciated that use of such a payment method is incentivizedfor both employer and employee). When payroll is disbursed 1401, it maybe directly deposited onto the employee's payment card for immediate use(for example, so that an employee may be immediately paid uponend-of-shift for their precise time worked, as described above withreference to FIG. 13). When the employee makes purchases using theircard, the employer processes the transaction and collects anytransaction fees 1402. If a payment card has insufficient funds toprocess a transaction 1403, the employee's loan risk profile may beretrieved to determine their creditworthiness and produce an offer 1404for a micro-loan. When an optimum micro-loan offer is produced, it maybe issued and the micro-loan payout deposited to the employee's paymentcard 1405 for use. The outstanding transaction may then be processed1406, factoring in the new balance after micro-loan issuance. Themicro-loan payment (or full payoff, depending on the details of thespecific offer that was selected and issued) may then be stored in theemployee's staff profile 1407, so that future payroll calculations mayautomatically deduct the loan payment when calculating precise net payas described above, with reference to FIG. 13. The employee's loan riskprofile may also be updated based on the loan issuance and payoff, forexample for reporting to credit agencies; this enables an employee toimprove their creditworthiness through regular processing ofmicro-loans, and facilitates a more-precise creditworthiness for theemployee that takes their real-time pay and spending into consideration.

FIG. 15 is a block diagram illustrating an exemplary architecture for apayment engine with an automated split payment aspect. This embodimentof a payment engine 220 comprises an updated staff profile 1500 thatfeatures a payment split profile. A payment split profile records thedesired payment dates of an employee as long as those dates fall withinany parameters the employer may set, e.g., daily, weekly, bi-weekly,etc. A payment split profile is edited by an employee on an employeedevice 1501 which may be of any user device type including a smartphone,tablet, laptop, or desktop, to give some examples. A bank server 1502represents a server used by a financial institution in order to processfinancial transactions between parties.

The payment engine 220 determines the net pay data for an employee overa period of time and communicates with that employee's device 1501 overa network to establish a desired split payment for that pay period.According to one aspect, a window on the employee device mayautomatically open when the user is logged in and editing the staffprofile 1500, the window providing instructions for the employee to editthe split payment profile. Additionally, a default pay period may be setby the employer in the event the employee does not set or fails to setdates within a set parameter. The employee device 1501 records thedesired payment dates and stores that into the staff profile 1500associated with the employee on a database that is read by the paymentengine 220, which then the payment engine 220 execute a payrolldisbursement on each of the desired payment dates by automaticallycommunicating with a bank server 1502. Should the business not have thefunds during a payroll disbursement, a micro-loan as describedpreviously may be obtained—using the microloan gateway 215.

This embodiment of the payment engine 220 coupled with an employeedevice 1501 and bank server 1502 may be integrated into any of theprevious embodiments described in figures above. Along the same lines,the payment engine 220 and common-embodiment components 212-216 may workas described in previous figures, i.e., the payment engine 220 mayoperate as an independent invention as described in this figure or beimplemented as described in any other embodiment herein.

According to an additional aspect, hours worked, or clock-in andclock-out times, of a staff member may be automatically entered into thestaff profile 1500 and may be further used to determine aspect of thepayroll. For example, an hourly worker may clock in and out on anetwork-connected device and in doing so, automatically logs their timeworked in their profile. The network-connected device may also be abiometric scanning device that uses the biometrics of staff toautomatically clock them in and out.

FIG. 16 is a flow diagram illustrating the steps of an exemplary methodfor zero-touch payroll management, detailing a process for providingautomated split payments to an employee. An initial first step 1601 isreceiving at a payment engine, a precise work time based on clock-in andclock-out times of a staff member. A second step 1602 comprisesretrieving a staff profile for the staff member from a database, whereinthe staff profile comprises a rate of pay, a payroll deduction, a taxrate, and a payment profile. A third step 1603 comprises updating thepayment profile by querying an employee device, the payment profilecomprising a preferred split payment and payment method set by theemployee device. A fourth step 1604 comprises calculating a net pay forthe staff member, the net pay being based on the information in thestaff profile. A fifth step 1605 specifics that if the net pay isgreater than a balance in a payment account, retrieve and process amicro-loan offer, wherein the micro-loan offer provides at least theamount difference between the balance and the net pay. A sixth step 1606comprises disbursing a payroll deposit, wherein the payment amount is inthe amount set by the split payment profile and transferred from thepayment account to the payment method.

Hardware Architecture

Generally, the techniques disclosed herein may be implemented onhardware or a combination of software and hardware. For example, theymay be implemented in an operating system kernel, in a separate userprocess, in a library package bound into network applications, on aspecially constructed machine, on an application-specific integratedcircuit (ASIC), or on a network interface card.

Software/hardware hybrid implementations of at least some of the aspectsdisclosed herein may be implemented on a programmable network-residentmachine (which should be understood to include intermittently connectednetwork-aware machines) selectively activated or reconfigured by acomputer program stored in memory. Such network devices may havemultiple network interfaces that may be configured or designed toutilize different types of network communication protocols. A generalarchitecture for some of these machines may be described herein in orderto illustrate one or more exemplary means by which a given unit offunctionality may be implemented. According to specific aspects, atleast some of the features or functionalities of the various aspectsdisclosed herein may be implemented on one or more general-purposecomputers associated with one or more networks, such as for example anend-user computer system, a client computer, a network server or otherserver system, a mobile computing device (e.g., tablet computing device,mobile phone, smartphone, laptop, or other appropriate computingdevice), a consumer electronic device, a music player, or any othersuitable electronic device, router, switch, or other suitable device, orany combination thereof. In at least some aspects, at least some of thefeatures or functionalities of the various aspects disclosed herein maybe implemented in one or more virtualized computing environments (e.g.,network computing clouds, virtual machines hosted on one or morephysical computing machines, or other appropriate virtual environments).

Referring now to FIG. 17, there is shown a block diagram depicting anexemplary computing device 10 suitable for implementing at least aportion of the features or functionalities disclosed herein. Computingdevice 10 may be, for example, any one of the computing machines listedin the previous paragraph, or indeed any other electronic device capableof executing software- or hardware-based instructions according to oneor more programs stored in memory. Computing device 10 may be configuredto communicate with a plurality of other computing devices, such asclients or servers, over communications networks such as a wide areanetwork a metropolitan area network, a local area network, a wirelessnetwork, the Internet, or any other network, using known protocols forsuch communication, whether wireless or wired.

In one aspect, computing device 10 includes one or more centralprocessing units (CPU) 12, one or more interfaces 15, and one or morebusses 14 (such as a peripheral component interconnect (PCI) bus). Whenacting under the control of appropriate software or firmware, CPU 12 maybe responsible for implementing specific functions associated with thefunctions of a specifically configured computing device or machine. Forexample, in at least one aspect, a computing device 10 may be configuredor designed to function as a server system utilizing CPU 12, localmemory 11 and/or remote memory 16, and interface(s) 15. In at least oneaspect, CPU 12 may be caused to perform one or more of the differenttypes of functions and/or operations under the control of softwaremodules or components, which for example, may include an operatingsystem and any appropriate applications software, drivers, and the like.

CPU 12 may include one or more processors 13 such as, for example, aprocessor from one of the Intel, ARM, Qualcomm, and AMD families ofmicroprocessors. In some aspects, processors 13 may include speciallydesigned hardware such as application-specific integrated circuits(ASICs), electrically erasable programmable read-only memories(EEPROMs), field-programmable gate arrays (FPGAs), and so forth, forcontrolling operations of computing device 10. In a particular aspect, alocal memory 11 (such as non-volatile random access memory (RAM) and/orread-only memory (ROM), including for example one or more levels ofcached memory) may also form part of CPU 12. However, there are manydifferent ways in which memory may be coupled to system 10. Memory 11may be used for a variety of purposes such as, for example, cachingand/or storing data, programming instructions, and the like. It shouldbe further appreciated that CPU 12 may be one of a variety ofsystem-on-a-chip (SOC) type hardware that may include additionalhardware such as memory or graphics processing chips, such as a QUALCOMMSNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly commonin the art, such as for use in mobile devices or integrated devices.

As used herein, the term “processor” is not limited merely to thoseintegrated circuits referred to in the art as a processor, a mobileprocessor, or a microprocessor, but broadly refers to a microcontroller,a microcomputer, a programmable logic controller, anapplication-specific integrated circuit, and any other programmablecircuit.

In one aspect, interfaces 15 are provided as network interface cards(NICs). Generally, NICs control the sending and receiving of datapackets over a computer network; other types of interfaces 15 may forexample support other peripherals used with computing device 10. Amongthe interfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces,graphics interfaces, and the like. In addition, various types ofinterfaces may be provided such as, for example, universal serial bus(USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radiofrequency (RF), BLUETOOTH™, near-field communications (e.g., usingnear-field magnetics), 802.11 (Wi-Fi), frame relay, TCP/IP, ISDN, fastEthernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) orexternal SATA (ESATA) interfaces, high-definition multimedia interface(HDMI), digital visual interface (DVI), analog or digital audiointerfaces, asynchronous transfer mode (ATM) interfaces, high-speedserial interface (HSSI) interfaces, Point of Sale (POS) interfaces,fiber data distributed interfaces (FDDIs), and the like. Generally, suchinterfaces 15 may include physical ports appropriate for communicationwith appropriate media. In some cases, they may also include anindependent processor (such as a dedicated audio or video processor, asis common in the art for high-fidelity A/V hardware interfaces) and, insome instances, volatile and/or non-volatile memory (e.g., RAM).

Although the system shown in FIG. 17 illustrates one specificarchitecture for a computing device 10 for implementing one or more ofthe aspects described herein, it is by no means the only devicearchitecture on which at least a portion of the features and techniquesdescribed herein may be implemented. For example, architectures havingone or any number of processors 13 may be used, and such processors 13may be present in a single device or distributed among any number ofdevices. In one aspect, a single processor 13 handles communications aswell as routing computations, while in other aspects a separatededicated communications processor may be provided. In various aspects,different types of features or functionalities may be implemented in asystem according to the aspect that includes a client device (such as atablet device or smartphone running client software) and server systems(such as a server system described in more detail below).

Regardless of network device configuration, the system of an aspect mayemploy one or more memories or memory modules (such as, for example,remote memory block 16 and local memory 11) configured to store data,program instructions for the general-purpose network operations, orother information relating to the functionality of the aspects describedherein (or any combinations of the above). Program instructions maycontrol execution of or comprise an operating system and/or one or moreapplications, for example. Memory 16 or memories 11, 16 may also beconfigured to store data structures, configuration data, encryptiondata, historical system operations information, or any other specific orgeneric non-program information described herein.

Because such information and program instructions may be employed toimplement one or more systems or methods described herein, at least somenetwork device aspects may include nontransitory machine-readablestorage media, which, for example, may be configured or designed tostore program instructions, state information, and the like forperforming various operations described herein. Examples of suchnontransitory machine-readable storage media include, but are notlimited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks; magneto-optical mediasuch as optical disks, and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory devices (ROM), flash memory (as is common in mobile devices andintegrated systems), solid state drives (SSD) and “hybrid SSD” storagedrives that may combine physical components of solid state and hard diskdrives in a single hardware device (as are becoming increasingly commonin the art with regard to personal computers), memristor memory, randomaccess memory (RAM), and the like. It should be appreciated that suchstorage means may be integral and non-removable (such as RAM hardwaremodules that may be soldered onto a motherboard or otherwise integratedinto an electronic device), or they may be removable such as swappableflash memory modules (such as “thumb drives” or other removable mediadesigned for rapidly exchanging physical storage devices),“hot-swappable” hard disk drives or solid state drives, removableoptical storage discs, or other such removable media, and that suchintegral and removable storage media may be utilized interchangeably.Examples of program instructions include both object code, such as maybe produced by a compiler, machine code, such as may be produced by anassembler or a linker, byte code, such as may be generated by forexample a JAVA™ compiler and may be executed using a Java virtualmachine or equivalent, or files containing higher level code that may beexecuted by the computer using an interpreter (for example, scriptswritten in Python, Perl, Ruby, Groovy, or any other scripting language).

In some aspects, systems may be implemented on a standalone computingsystem. Referring now to FIG. 18, there is shown a block diagramdepicting a typical exemplary architecture of one or more aspects orcomponents thereof on a standalone computing system. Computing device 20includes processors 21 that may run software that carry out one or morefunctions or applications of aspects, such as for example a clientapplication 24. Processors 21 may carry out computing instructions undercontrol of an operating system 22 such as, for example, a version ofMICROSOFT WINDOWS™ operating system, APPLE macOS™ or iOS™ operatingsystems, some variety of the Linux operating system, ANDROID™ operatingsystem, or the like. In many cases, one or more shared services 23 maybe operable in system 20 and may be useful for providing common servicesto client applications 24. Services 23 may for example be WINDOWS™services, user-space common services in a Linux environment, or anyother type of common service architecture used with operating system 21.Input devices 28 may be of any type suitable for receiving user input,including for example a keyboard, touchscreen, microphone (for example,for voice input), mouse, touchpad, trackball, or any combinationthereof. Output devices 27 may be of any type suitable for providingoutput to one or more users, whether remote or local to system 20, andmay include for example one or more screens for visual output, speakers,printers, or any combination thereof. Memory 25 may be random-accessmemory having any structure and architecture known in the art, for useby processors 21, for example to run software. Storage devices 26 may beany magnetic, optical, mechanical, memristor, or electrical storagedevice for storage of data in digital form (such as those describedabove, referring to FIG. 18). Examples of storage devices 26 includeflash memory, magnetic hard drive, CD-ROM, and/or the like.

In some aspects, systems may be implemented on a distributed computingnetwork, such as one having any number of clients and/or servers.Referring now to FIG. 19, there is shown a block diagram depicting anexemplary architecture 30 for implementing at least a portion of asystem according to one aspect on a distributed computing network.According to the aspect, any number of clients 33 may be provided. Eachclient 33 may run software for implementing client-side portions of asystem; clients may comprise a system 20 such as that illustrated inFIG. 18. In addition, any number of servers 32 may be provided forhandling requests received from one or more clients 33. Clients 33 andservers 32 may communicate with one another via one or more electronicnetworks 31, which may be in various aspects any of the Internet, a widearea network, a mobile telephony network (such as CDMA or GSM cellularnetworks), a wireless network (such as Wi-Fi, WiMAX, LTE, and so forth),or a local area network (or indeed any network topology known in theart; the aspect does not prefer any one network topology over anyother). Networks 31 may be implemented using any known networkprotocols, including for example wired and/or wireless protocols.

In addition, in some aspects, servers 32 may call external services 37when needed to obtain additional information, or to refer to additionaldata concerning a particular call. Communications with external services37 may take place, for example, via one or more networks 31. In variousaspects, external services 37 may comprise web-enabled services orfunctionality related to or installed on the hardware device itself. Forexample, in one aspect where client applications 24 are implemented on asmartphone or other electronic device, client applications 24 may obtaininformation stored in a server system 32 in the cloud or on an externalservice 37 deployed on one or more of a particular enterprise's oruser's premises. In addition to local storage on servers 32, remotestorage 38 may be accessible through the network(s) 31.

In some aspects, clients 33 or servers 32 (or both) may make use of oneor more specialized services or appliances that may be deployed locallyor remotely across one or more networks 31. For example, one or moredatabases 34 in either local or remote storage 38 may be used orreferred to by one or more aspects. It should be understood by onehaving ordinary skill in the art that databases in storage 34 may bearranged in a wide variety of architectures and using a wide variety ofdata access and manipulation means. For example, in various aspects oneor more databases in storage 34 may comprise a relational databasesystem using a structured query language (SQL), while others maycomprise an alternative data storage technology such as those referredto in the art as “NoSQL” (for example, HADOOP CASSANDRA™, GOOGLEBIGTABLE™, and so forth). In some aspects, variant databasearchitectures such as column-oriented databases, in-memory databases,clustered databases, distributed databases, or even flat file datarepositories may be used according to the aspect. It will be appreciatedby one having ordinary skill in the art that any combination of known orfuture database technologies may be used as appropriate, unless aspecific database technology or a specific arrangement of components isspecified for a particular aspect described herein. Moreover, it shouldbe appreciated that the term “database” as used herein may refer to aphysical database machine, a cluster of machines acting as a singledatabase system, or a logical database within an overall databasemanagement system. Unless a specific meaning is specified for a givenuse of the term “database”, it should be construed to mean any of thesesenses of the word, all of which are understood as a plain meaning ofthe term “database” by those having ordinary skill in the art.

Similarly, some aspects may make use of one or more security systems 36and configuration systems 35. Security and configuration management arecommon information technology (IT) and web functions, and some amount ofeach are generally associated with any IT or web systems. It should beunderstood by one having ordinary skill in the art that anyconfiguration or security subsystems known in the art now or in thefuture may be used in conjunction with aspects without limitation,unless a specific security 36 or configuration system 35 or approach isspecifically required by the description of any specific aspect.

FIG. 20 shows an exemplary overview of a computer system 40 as may beused in any of the various locations throughout the system. It isexemplary of any computer that may execute code to process data. Variousmodifications and changes may be made to computer system 40 withoutdeparting from the broader scope of the system and method disclosedherein. Central processor unit (CPU) 41 is connected to bus 42, to whichbus is also connected memory 43, nonvolatile memory 44, display 47,input/output (I/O) unit 48, and network interface card (NIC) 53. I/Ounit 48 may, typically, be connected to peripherals such as a keyboard49, pointing device 50, hard disk 52, real-time clock 51, a camera 57,and other peripheral devices. NIC 53 connects to network 54, which maybe the Internet or a local network, which local network may or may nothave connections to the Internet. The system may be connected to othercomputing devices through the network via a router 55, wireless localarea network 56, or any other network connection. Also shown as part ofsystem 40 is power supply unit 45 connected, in this example, to a mainalternating current (AC) supply 46. Not shown are batteries that couldbe present, and many other devices and modifications that are well knownbut are not applicable to the specific novel functions of the currentsystem and method disclosed herein. It should be appreciated that someor all components illustrated may be combined, such as in variousintegrated applications, for example Qualcomm or Samsungsystem-on-a-chip (SOC) devices, or whenever it may be appropriate tocombine multiple capabilities or functions into a single hardware device(for instance, in mobile devices such as smartphones, video gameconsoles, in-vehicle computer systems such as navigation or multimediasystems in automobiles, or other integrated hardware devices).

In various aspects, functionality for implementing systems or methods ofvarious aspects may be distributed among any number of client and/orserver components. For example, various software modules may beimplemented for performing various functions in connection with a systemof any particular aspect, and such modules may be variously implementedto run on server and/or client components.

What is claimed is:
 1. A system for zero-touch payroll management,comprising: a payment engine comprising a first plurality of programminginstructions stored in the memory and operating on the processor,wherein the first plurality of programming instructions, when operatingon the processor, causes the computer system to: receive a precise worktime based on clock-in and clock-out times of a staff member; retrieve astaff profile for the staff member from a database, wherein the staffprofile comprises a rate of pay, a payroll deduction, a tax rate, and apayment profile; update the payment profile by querying an employeedevice, the payment profile comprising a set of dates for a preferredsplit payment and payment method set by the employee device; calculate anet pay for the staff member, the net pay being based on the informationin the staff profile; if the net pay is greater than a balance in apayment account, retrieve and process a micro-loan offer, wherein themicro-loan offer provides at least the amount difference between thebalance and the net pay; and disburse a payroll deposit, wherein thepreferred split payment amount is transferred from the payment accountto the payment method.
 2. The system of claim 1, wherein the paymentmethod is a payment card.
 3. The system of claim 2, wherein the paymentengine processes a transaction made using the payment card.
 4. Thesystem of claim 1, wherein the payment engine: retrieves a loan riskprofile for the staff member; issues a micro-loan based on the loan riskprofile; and updates the staff profile based on the issued micro-loan,wherein the updated staff profile comprises at least a payroll deductionbased on the micro-loan issued.
 5. The system of claim 1, wherein theclock-in or clock out time is received from a biometric scanning device.6. A method for zero-touch payroll management, comprising the steps of:receiving at a payment engine, a precise work time based on clock-in andclock-out times of a staff member; retrieving a staff profile for thestaff member from a database, wherein the staff profile comprises a rateof pay, a payroll deduction, a tax rate, and a payment profile; updatingthe payment profile by querying an employee device, the payment profilecomprising a set of dates for a preferred split payment and paymentmethod set by the employee device; calculating a net pay for the staffmember, the net pay being based on the information in the staff profile;if the net pay is greater than a balance in a payment account, retrieveand process a micro-loan offer, wherein the micro-loan offer provides atleast the amount difference between the balance and the net pay; anddisbursing a payroll deposit, wherein the preferred split payment amountis transferred from the payment account to the payment method.
 7. Themethod of claim 6, wherein the payment method is a payment card.
 8. Themethod of claim 7, wherein the payment engine processes a transactionmade using the payment card.
 9. The method of claim 6, furthercomprising the steps of: retrieving a loan risk profile for the staffmember; issuing a micro-loan based on the loan risk profile; andupdating the staff profile based on the issued micro-loan, wherein theupdated staff profile comprises at least a payroll deduction based onthe micro-loan issued.
 10. The method of claim 6, wherein the clock-inor clock out time is received from a biometric scanning device.